A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

Download Full-text

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-9413-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 9413-9452 ◽

Cited By ~ 1

Author(s):

M.-H. Ahn ◽

D. Han ◽

H.-Y. Won ◽

V. Morris

Keyword(s):

Water Vapor Pressure ◽

Microwave Radiometer ◽

Surface Air Temperature ◽

Detection Algorithm ◽

Probability Of Detection ◽

Cloud Base ◽

Cloud Detection ◽

Clear Sky ◽

Spectral Test ◽

Free Data

Abstract. For a better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of the clear sky condition. It is designated as cloud free data only when both the spectral and temporal tests confirm a cloud free data. Overall, most of the thick and uniform clouds are successfully screened out by the spectral test, while the broken and fast-varying clouds are screened out by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for 6 months, from January 2013 to June 2013. The overall proportion correct is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of failures occur when the new algorithm detects clouds while the ceilometer does not detect, resulting in different values of the probability of detection with different cloud base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

Download Full-text

Combination of AIRS Dual CO2 Absorption Bands to Develop an Ice Clouds Detection Algorithm in Different Atmospheric Layers

Remote Sensing ◽

10.3390/rs12010006 ◽

2019 ◽

Vol 12 (1) ◽

pp. 6 ◽

Cited By ~ 1

Author(s):

Liwen Wang ◽

Youfei Zheng ◽

Chao Liu ◽

Zeyi Niu ◽

Jingxin Xu ◽

...

Keyword(s):

Linear Relationship ◽

Weighting Function ◽

Detection Algorithm ◽

Probability Of Detection ◽

Orthogonal Polarization ◽

Co2 Absorption ◽

Cloud Detection ◽

Absorption Bands ◽

Ice Clouds ◽

Ice Cloud

The use of infrared (IR) sensors to detect clouds in different layers of the atmosphere is a big challenge, especially for ice clouds. This study aims to improve ice cloud detection using Lin’s algorithm and apply it to Atmospheric Infrared Sounder (AIRS). To achieve these objectives, the scattering and emission characteristics of clouds as perceived by AIRS longwave infrared (LWIR, ~15 μm) and shortwave infrared (SWIR, ~4.3 μm) CO2 absorption bands are applied for ice cloud detection. Hence, the weighting function peak (WFP), cut-off pressure, and correlation coefficients between the brightness temperatures (BTs) of LWIR and SWIR channels are used to pair the LWIR and SWIR channels. After that, the linear relationship between the clear-sky BTs of the paired LWIR and SWIR channels is established by the cloud scattering and emission Index (CESI). However, the linear relationship fails in the presence of ice clouds. Comparing these results with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations show that the probability of detection of ice clouds for Pair-8 (WFP~330hPa), Pair-19 (WFP~555hPa), and Pair-24 (WFP~866hPa) are 0.63, 0.71, and 0.73 in the daytime and 0.46, 0.62, and 0.7 in the nighttime at a false alarm rate of 0.1 when ice clouds top pressure above 330 hPa, 555 hPa, and 866 hPa, respectively. Furthermore, the thresholds of the three pairs are 2.4 K, 3 K, and 8.7 K in the daytime and 1.7 K, 1.7 K, and 4.4 K in the nighttime at the highest Heike Skill Score (HSS). The error of HSS values based on thresholds of ice clouds is between 0.01 and 0.02 which is comparable with the ice cloud detection results in both day and night conditions. It is shown that Pair-8 (WFP~330hPa) can detect opaque and thick ice clouds above its WFP altitude over the tropical areas but it is unable to observe ice clouds over the mid-latitude while Pair-19 and Pair-24 can identify ice clouds above their WFP altitude.

Download Full-text

Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0130.1 ◽

2021 ◽

Author(s):

Theodore M. McHardy ◽

James R. Campbell ◽

David A. Peterson ◽

Simone Lolli ◽

Richard L. Bankert ◽

...

Keyword(s):

Precipitable Water ◽

Detection Algorithm ◽

Machine Learning Techniques ◽

Orthogonal Polarization ◽

Cirrus Cloud ◽

Cloud Detection ◽

Cloud Optical Depth ◽

Clear Sky ◽

Learning Techniques ◽

Microphysical Property

AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

Download Full-text

Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D

Remote Sensing ◽

10.3390/rs11232811 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2811 ◽

Cited By ~ 1

Author(s):

Lima ◽

Prijith ◽

Sesha Sai ◽

Rao ◽

Niranjan ◽

...

Keyword(s):

Geostationary Satellite ◽

Probability Of Detection ◽

Quality Data ◽

Orthogonal Polarization ◽

Cloud Detection ◽

Pixel Resolution ◽

Clear Sky ◽

Hit Rate ◽

Cloud Mask ◽

Cloud Top Temperature

Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies.

Download Full-text

Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-5-8189-2012 ◽

2012 ◽

Vol 5 (6) ◽

pp. 8189-8222 ◽

Cited By ~ 1

Author(s):

X. Wang ◽

W. Li ◽

Y. Zhu ◽

B. Zhao

Keyword(s):

Complex Terrain ◽

Detection Algorithm ◽

Difficult Problem ◽

Probability Of Detection ◽

Official Method ◽

Cloud Detection ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces ◽

Northwest Region ◽

Cloud Mask

Abstract. The existence of various land surfaces has always been a difficult problem for researchers who study cloud detection using satellite observations, especially over bright surfaces such as snow and desert. To improve the cloud mask result over complex terrain, an unbiased daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. Based on the statistical seasonal threshold tests, the algorithm consists of six main channels centered on the wavelengths of 0.63, 0.865, 10.8, 1.595, 0.455, and 1.36 μm. The combination of the unbiased algorithm and the specific threshold tests for special surfaces has effectively improved the cloud mask results over complex terrain and decreased the false identifications of clouds. The visual images over snow and desert adopting the proposed scheme exhibit better correlations with true-color images than do the VIRR official cloud mask results. The validation with the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product shows that the probability of detection for clear-sky regions over snow of the new scheme has increased nearly five times over the official method, and the false-alarm ratio for cloudy areas over desert has reduced by half compared with the official result. With regard to comparisons between ground measurements and cloud mask results, this approach also provides acceptable correspondence with the ground observations except for some cases, which are mainly obscured by cirrus clouds.

Download Full-text

A RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection

10.5194/amt-2016-222 ◽

2016 ◽

Author(s):

Jun Yang ◽

Qilong Min ◽

Weitao Lu ◽

Ying Ma ◽

Wen Yao ◽

...

Keyword(s):

Detection Algorithm ◽

Qualitative Assessment ◽

The Sun ◽

Cloud Detection ◽

Recognition Error ◽

Atmospheric Scattering ◽

Clear Sky ◽

Different Types ◽

Rgb Images ◽

The Difference

Abstract. The inhomogeneous sky background presents a great challenge for accurate cloud recognition from the total sky images. A channel operation was introduced in this study to produce a new composite channel in which the difference of atmospheric scattering has been removed and a homogeneous sky background can be obtained. Following this, a new cloud detection algorithm was proposed, which combined the merits of the differencing and threshold methods and named "differencing and threshold combination algorithm (DTCA)". Firstly, the channel operation was applied to transform 3-D RGB images to the new channel, then the circumsolar saturated pixels and its circularity were used to judge whether the sun is visible or not in the image. When the sun is obscured, a single threshold can be used to identify cloud pixels, and, when the sun is visible in the image, the true clear sky background differencing algorithm is adopted to detect clouds. The qualitative assessment for eight different total sky images shows the DTCA algorithm obtained satisfactory cloud identification effectiveness for thin clouds and in the circumsolar and near-horizon regions. Quantitative evaluation also shows the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds, with an average recognition error rate of 8.7 %.

Download Full-text

A total sky cloud detection method using real clear sky background

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-13073-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 13073-13098 ◽

Cited By ~ 1

Author(s):

J. Yang ◽

Q. Min ◽

W. Lu ◽

Y. Ma ◽

W. Yao ◽

...

Keyword(s):

Detection Algorithm ◽

Brightness Distribution ◽

Detection Methods ◽

Cloud Detection ◽

Image Brightness ◽

Clear Sky ◽

Sensitivity Tests ◽

Thin Cloud ◽

Distribution Sensitivity ◽

Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show, as long as the positions of the sun in the two images are the same, the cloud detection results are satisfactory. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

Download Full-text

A total sky cloud detection method using real clear sky background

Atmospheric Measurement Techniques ◽

10.5194/amt-9-587-2016 ◽

2016 ◽

Vol 9 (2) ◽

pp. 587-597 ◽

Cited By ~ 8

Author(s):

Jun Yang ◽

Qilong Min ◽

Weitao Lu ◽

Ying Ma ◽

Wen Yao ◽

...

Keyword(s):

Detection Algorithm ◽

Brightness Distribution ◽

Detection Methods ◽

Cloud Detection ◽

Image Brightness ◽

Clear Sky ◽

Sensitivity Tests ◽

Thin Cloud ◽

Distribution Sensitivity ◽

Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods, including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show that the cloud detection results are satisfactory when the two images have the same solar positions. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

Download Full-text

Ground-Based Temperature and Humidity Profiling Using Spectral Infrared and Microwave Observations. Part II: Actual Retrieval Performance in Clear-Sky and Cloudy Conditions

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0005.1 ◽

2015 ◽

Vol 54 (11) ◽

pp. 2305-2319 ◽

Cited By ~ 24

Author(s):

W. G. Blumberg ◽

D. D. Turner ◽

U. Löhnert ◽

S. Castleberry

Keyword(s):

Remote Sensing ◽

Boundary Layer ◽

Temporal Evolution ◽

Microwave Radiometer ◽

Optimal Estimation ◽

Cloud Base ◽

Retrieval Performance ◽

Retrieval Accuracy ◽

Clear Sky ◽

Observing Systems

AbstractAlthough current upper-air observing systems provide an impressive array of observations, many are deficient in observing the temporal evolution of the boundary layer thermodynamic profile. Ground-based remote sensing instruments such as the multichannel microwave radiometer (MWR) and Atmospheric Emitted Radiance Interferometer (AERI) are able to provide profiles of temperature and water vapor through the boundary layer at 5-min resolution or better. Previous work compared these instruments through optimal-estimation retrievals on simulated clear-sky spectra to evaluate the retrieval accuracy and information content of each instrument. In this study, this method is duplicated using real observations from collocated MWR and AERI instruments from a field campaign in southwestern Germany. When compared with radiosondes, this study confirms the previous results that AERI retrievals are more accurate than MWR retrievals in clear-sky and below-cloud-base profiling. These results demonstrate that the AERI has nearly 2 times as much information as the MWR.

Download Full-text

An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection

Atmospheric Measurement Techniques ◽

10.5194/amt-10-1191-2017 ◽

2017 ◽

Vol 10 (3) ◽

pp. 1191-1201 ◽

Cited By ~ 6

Author(s):

Jun Yang ◽

Qilong Min ◽

Weitao Lu ◽

Ying Ma ◽

Wen Yao ◽

...

Keyword(s):

Quantitative Evaluation ◽

Detection Algorithm ◽

Qualitative Assessment ◽

The Sun ◽

Cloud Detection ◽

Atmospheric Scattering ◽

Clear Sky ◽

Different Types ◽

The Difference ◽

Rgb Image

Abstract. The inhomogeneous sky background presents a great challenge for accurate cloud recognition from the total-sky images. A channel operation was introduced in this study to produce a new composite channel in which the difference of atmospheric scattering has been removed and a homogeneous sky background can be obtained. Following this, a new cloud detection algorithm was proposed that combined the merits of the differencing and threshold methods, named differencing and threshold combination algorithm (DTCA). Firstly, the channel operation was applied to transform 3-D RGB image to the new channel, then the circumsolar saturated pixels and its circularity were used to judge whether the sun is visible or not in the image. When the sun is obscured, a single threshold can be used to identify cloud pixels. If the sun is visible in the image, the true clear-sky background differencing algorithm is adopted to detect clouds. The qualitative assessment for eight different total-sky images shows the DTCA algorithm obtained satisfactory cloud identification effectiveness for thin clouds and in the circumsolar and near-horizon regions. Quantitative evaluation also shows that the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds and performed well under both visible sun and blocked sun conditions.

Download Full-text